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2019

Understanding Neural Architecture Search Techniques

Adam, George, Lorraine, Jonathan

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Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently.

This is because of the potential to remove human experts from the design loop which can reduce costs and decrease time to model deployment.
Neural architecture search (NAS) techniques have improved significantly in their computational efficiency since the original NAS was proposed.
This reduction in computation is enabled via weight sharing such as in Efficient Neural Architecture Search (ENAS).

Understanding Neural Architecture Search Techniques · Around

Built on

Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Original
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
Progressive Neural Architecture Search
Original
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2017
Earlier work this paper cites.

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SELF-TUNING NETWORKS: BILEVEL OPTIMIZATION OF HYPERPARAMETERS US-ING STRUCTURED BEST-RESPONSE FUNCTIONS
Matthew Mackay, Paul Vicol, Jon Lorraine, David Duvenaud, and Roger Grosse
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Then

Random search and reproducibility for neural architecture search
Original
Liam Li and Ameet Talwalkar · 2019
Closest in time.
Evaluating the search phase of neural architecture search
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Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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